Divergent Evolution of Malignant Subclones Maintains a Balance between Induced Aggressiveness and Intrinsic Drug Resistance in T-cell Cancer
Bibliographic record
Abstract
Evolution and outgrowth of drug-resistant cancer cells are common causes of treatment failure. Patients with leukemic cutaneous T-cell lymphoma have a poor prognosis because of the development of drug resistance and severe bacterial infections. In this study, we show that most patients with leukemic cutaneous T-cell lymphoma harbor multiple genetically distinct subclones that express an identical clonal antigen receptor but display distinct phenotypes and functional properties. These coexisting malignant subclones exhibit differences in tissue homing, metabolism, and cytokine expression and respond differently to extrinsic factors like Staphylococcus aureus and cancer drugs. Indeed, although S. aureus toxins selectively enhance activation and proliferation of certain subclones, these responsive subclones are also the most intrinsically sensitive to cancer drugs when the stimuli are removed. Consequently, although the divergent evolution of malignant subclones drives aggressiveness, adaptability, and drug resistance by removing extrinsic stimuli and mapping malignant subclones, we can expose inherent vulnerabilities that can be exploited in the treatment of these cancers. SIGNIFICANCE: Cancer cells have inherent disparity in hallmark traits, such as aggressiveness and intrinsic drug resistance. We show that segregation of hallmark traits on different coexisting subclones is common and augments adaptability, aggressiveness, and drug resistance of the overall cancer population. Importantly, this segregation exposes vulnerabilities that can be exploited in individualized therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".